--- a
+++ b/Learning/model.py
@@ -0,0 +1,38 @@
+import torch
+import torch.nn as nn
+
+class DeepDrug3D(nn.Module):
+    def __init__(self, in_channel):
+        super(DeepDrug3D, self).__init__()
+
+        self.conv1 = nn.Conv3d(in_channel, 64, 5)
+        self.conv2 = nn.Conv3d(64, 64, 3)
+
+        self.pool = nn.MaxPool3d((2,2,2), stride=None)
+        self.fc1 = nn.Linear(64*13*13*13, 128)
+        self.fc2 = nn.Linear(128,3)
+
+    def reset_parameters(self):
+        self.conv1.reset_parameters()
+        self.conv2.reset_parameters()
+        self.fc1.reset_parameters()
+        self.fc2.reset_parameters()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = nn.LeakyReLU(negative_slope=0.1)(x)
+        x = nn.Dropout(p=0.2)(x)
+
+        x = self.conv2(x)
+        x = nn.LeakyReLU(negative_slope=0.1)(x)
+
+        x = self.pool(x)
+        x = nn.Dropout(p=0.4)(x)
+
+        x = torch.flatten(x, start_dim=1)
+        x = self.fc1(x)
+        x = nn.LeakyReLU(negative_slope=0.1)(x)
+        x = nn.Dropout(p=0.4)(x)
+
+        x = self.fc2(x)
+        return x
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